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Coastal Modelling Environment Version Coastal Modelling Environment version 1.0: a framework for integrating landform-specific component models in order to simulate decadal to centennial morphological changes on complex coasts Andrés Payo1, 2, 3*, David Favis-Mortlock1, Mark Dickson4, Jim W. Hall1, Martin D. Hurst3, 5†, Mike 5 J.A. Walkden6, Ian Townend7, Matthew C. Ives1, Robert J. Nicholls2, Michael A. Ellis3 1Oxford University Centre for the Environment, South Parks Road, Oxford, OX1 3QY, UK 2Faculty of Eng. & the Env. Energy & Climate Change, Southampton Univ., Southampton, SO17 1BJ, UK 3British Geological Survey, Keyworth, NG12 5GD, UK (current address) * 4School of Env. University of Auckland, 10 Symonds St, Auckland Private Bag 92019, NZ 10 5University of Glasgow, East Quad, Glasgow, G12 8QQ, UK (current address) † 6WSP|Parsons Brinckerhoff, Keble House, Southernhay Gardens, Exeter EX1 1NT, UK 7National Oceanography Centre, Southampton University, SO14 3ZH UK Correspondence to: Andres Payo ([email protected]) Abstract. The ability to model morphological changes on complex, multi-landform, coasts during decadal to centennial time 15 scales is essential for sustainable coastal management world-wide. One approach involves coupling of landform-specific simulation models (e.g. cliffs, beaches, dunes, estuaries, etc.) that have been independently developed. An alternative, novel, approach explored in this paper is to capture the essential characteristics of the landform-specific models using a common spatial representation within an appropriate software framework. This avoid the problems that result from the model- coupling approach due to between-model differences in the conceptualisations of geometries, volumes and locations of 20 sediment. In the proposed framework, the Coastal Modelling Environment (CoastalME), change in coastal morphology is represented by means of dynamically linked raster and geometrical objects. A grid of raster cells provides the data structure for representing quasi-3D spatial heterogeneity and sediment conservation. Other geometrical objects (lines, areas and volumes) that are consistent with, and derived from, the raster structure represent a library of coastal elements (e.g. shoreline, beach profiles and estuary volumes) as required by different landform-specific models. As a proof-of-concept, we 25 illustrate the capabilities of an initial version of CoastalME by integrating a cliff-beach model and two wave propagation approaches. We verify that CoastalME can reproduce behaviours of the component landform-specific models. Additionally, the integration of these component models within the CoastalME framework reveals behaviours that emerge from the interaction of landforms, which have not previously been captured, such as the influence of the regional bathymetry on the local alongshore sediment transport gradient, the effect on coastal change on an undefended coastal segment and on sediment 30 bypassing of coastal structures. 1 1 Introduction Coastal managers worldwide must plan for decadal to centennial time horizons [e.g. Nicholls et al., 2012] and may well need to also assess longer-term adaptation measures [Brown et al., 2014; Hall et al., 2012]. However, quantitative prediction of morphological coastal changes at meso-scales (decades to centuries and 10s to 100s of km) is scientifically challenging. 5 Physics-based, reductionist models that represent small-scale processes have proven to be of limited use in this task, both because of the accumulation of small errors over long timescales [de Vriend et al., 1993], because of the omission of processes that govern long-term change [Murray, 2007; Werner, 2003] and computational limitations [Daly et al., 2015]. Faced with this impasse, coastal geomorphologists have begun to adopt simpler behaviourally based approaches or Large Scale Coastal Behavioural (LSCB) models [Terwindt and Battjes, 1990]. LSCB models seek to represent the main physical 10 governing processes at appropriate time and space scales [Cowell et al., 1995; French et al., 2016b; Murray, 2013]. Central to these approaches has been selective characterisation of the coastline: thus, we have seen the development of models that simulate the temporal evolution of a range of individual elements of coastal morphology, such as coastal profiles, shorelines or estuary volumes. However, modelling of complex coastlines involving multiple landforms (for example, beaches and tidal inlets) requires consideration of interactions between the component landforms, subject to the principles of mass 15 conservation. This is difficult: modelling these interactions is still not commonplace. One possible way forward is the development and use of model-to-model interfaces; software wrappers that allow coupling of independently-developed component models [Moore and Hughes, 2016; Sutherland et al., 2014]. Significant effort has been oriented in this direction during the last decade, in particular by the Open Modelling Interface (OpenMI) and 20 Community Surface Dynamics Modelling System (CSDMS). OpenMI emerged from the water sector as a way to link existing stand-alone models that were not originally designed to work together [Gregersen et al., 2005], while CSDMS draws on a large pool of well understood open-access models [Hutton et al., 2014]. The promise of OpenMI and CSDMS is to provide a unified system to link various models in order to explore broader system behaviour. However, a range of challenges becomes apparent when linking component models in this way. These include difficulties associated with fully 25 accounting for the cumulative effect of various assumptions made by, and uncertainties in, the constituent models, and non- trivial technical issues concerning variable names and units [Peckham et al., 2013]. Such software-coupling frameworks are themselves agnostic with regard to the spatial structures of component models. This creates a further significant challenge when coupling existing LSCB models due to fundamental between-model differences in the conceptualisations of geometries, volumes and locations of sediment. For example, the Soft Cliff and Platform Evolution [SCAPE, Walkden and 30 Hall, 2011] model assumes a beach of finite thickness perched at the top of the bedrock shore profile, while one-line approaches assume infinite beach thickness [Payo et al., 2015]. Similarly, a 2-D estuary model uses the bathymetry to define the form as a continuum, whereas an aggregated model, such as ASMITA [Stive et al., 1997; Townend et al., 2016], uses 2 only the volume of user defined constituent elements. In this context, coastal modellers need an alternative approach to model integration. We suggest that integrated modelling must go beyond the software coupling issues that have been the focus of OpenMI and CSDMS. Instead, as argued by Raper and Livingstone [1995], integrated modelling should deal more directly with the 5 semantics of the various entities modelled. We propose a way to address this: by means of a modular, object-oriented framework in which these entities are the primary constructs. In other words, the objects that interact within the model framework should correspond to the main real-world constructs considered by coastal scientists and managers. Figure 1 illustrates the modelling approach underpinning the proposed modelling framework; representation of space, and of the changes occurring within its spatial domain, involves both raster (i.e. grid) and vector (i.e. coastline, profile and sediment 10 sharing polygons) representations of spatial objects. This is commonplace in modern GIS packages. What is relatively unusual, however, is that in the proposed framework data is routinely and regularly transformed between these two representations during each time step of a simulation. In this paper, we provide a detailed description of the proposed Coastal Modelling Environment (CoastalME). We also 15 provide a proof-of-concept illustration of its integrative capacity by unifying independently developed cliff, beach and wave propagation models. Validation of the geomorphological outcomes of model runs against real-world data will be the subject of a future study. This manuscript is organized in six sections. In Section 2, we have outlined the background and rationale for the proposed coastal modelling environment. In Section 3, we explain in detail the proposed framework, including the representation of space and time, inputs and outputs, within time steps main operations, treatment of the domain boundary 20 conditions, implementation and CoastalME modular design. In Section 4, we present some simulation results to illustrate how the different model components integrated in this first composition interact to produce realistic coastal morphological changes and we discuss its advantages and limitations. In Section 5, we summarize the main conclusions. In Section 6, we outline the main websites and weblinks from which the code, the input files used for the test cases and a dedicated wiki-site are available. 25 2 Background and rationale of the proposed coastal modelling environment 2.1 Determinants of large scale coastal behaviour The dynamic behaviour observed in coastal geomorphology is the result of complex feedback relationships linking hydrology, sediment transport and resulting bed evolution, driven by time-variant or stationary boundary conditions and 30 modulated by the underlying geology [Cowell et al., 2003]. While coastal scientists do not have a full understanding of the key processes that control the dynamics of
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